The hardness of cemented carbides is a fundamental property that plays a significant role in their design, preparation, and application evaluation. This study aims to identify the critical factors affecting the hardness of WC-Co cemented carbides and develop a high-throughput predictive model for hardness. A dataset consisting of raw material composition, sintering parameters and characterization results of cemented carbides is constructed in which the hardness of cemented carbide is set as the target variable. By analyzing the Pearson correlation coefficient, Shapley additive explanations (SHAP) results, WC grain size and Co content are determined to be the key characteristics influencing the hardness of cemented carbide. Subsequently, machine learning models such as support vector regression (SVR), polynomial regression (PR), gradient boosting decision tree (GBDT), and random forest (RF) are optimized to construct prediction models for hardness. Evaluations using 10-fold cross-validation demonstrate that the GBDT algorithm model exhibits the highest accuracy and strong generalization capability, making it most suitable for predicting and analyzing the hardness of cemented carbides. Based on predictions from GBDT algorithm model, PR algorithm model is established to achieve high-precision interpretable prediction of the hardness of cemented carbides. As a result, a quantitative relationship between hardness and Co content and WC grain size is obtained, demonstrating that reducing grain size and Co content is the key to obtaining high hardness of cemented carbide. This research provides a data-driven method for accurately and efficiently predicting cemented carbide properties, presenting valuable insights for the design and development of high-performance cemented carbide materials.